CN108564249A - The power distribution network confidence peak clipping benifits appraisal procedure of meter and distributed photovoltaic randomness - Google Patents

Abstract

The present invention provides the power distribution network confidence peak clipping benifits appraisal procedure of meter and distributed photovoltaic randomness, includes the following steps：（1）Obtain parameter of double--layer grids, load model and the photovoltaic output model of power distribution network；（2）Obtain the node serial number and node capacity of distributed photovoltaic access；（3）Using the daily load curve and photovoltaic power curve of each node of Monte Carlo method sampled analog；（4）Calculate the confidence day peak clipping degree of each node；（5）Foundation while meter and the probability assessment model of substation, circuit and distribution transforming three classes equipment peak clipping benifits；（6）The confidence day peak clipping benifits expectation of power distribution network is assessed.The present invention propose it is a kind of meter and distributed photovoltaic randomness power distribution network confidence peak clipping benifits appraisal procedure, it can be used for carrying out the assessment of probability ground peak clipping benifits to the power distribution network containing distributed photovoltaic, assessment models can also effectively pick out influence difference of the distributed photovoltaic with different location and the access of different voltages grade to distribution peak clipping benifits.

Description

Evaluation method of confidence and peak-shaving benefits of distribution network considering distributed photovoltaic randomness

technical field

The invention relates to the field of evaluation methods for peak-shaving benefits of distribution networks, in particular to a probability evaluation method that takes into account the randomness of distributed photovoltaics.

Background technique

In the relevant research on the optimal planning and optimal operation of distribution network, the distribution network peak-shaving benefit index is often used as an important part of the total revenue objective function of the grid side. The penetration rate of distributed photovoltaics as a clean power source connected to the distribution network is increasing day by day, but due to its output being affected by uncertain factors such as light, it will have strong randomness. Therefore, it is not appropriate to evaluate the deterministic peak-shaving benefits of distribution networks with distributed photovoltaics.

At present, the peak-shaving benefit evaluation model of the distribution network basically does not consider the impact of distributed photovoltaic randomness on the evaluation results. At the same time, the evaluation calculation model is relatively rough, or ignores the peak-shaving benefits of lines and distribution transformers and only considers the peak-shaving benefits of substations , or use the average peak-shaving benefits of distribution transformers, lines and substations to calculate, and the value of the average peak-shaving benefits for different distribution networks is also difficult to determine.

To sum up, the existing assessment models and methods for peak-shaving benefit of distribution network still need further improvement.

Contents of the invention

The purpose of the present invention is to improve the evaluation model of distribution network peak shaving benefits. On the basis of taking into account the randomness of distributed photovoltaics, it aims to provide a better ability to identify different locations and voltage levels of distributed photovoltaic access distribution networks. Evaluation methods for peak shaving benefits.

The present invention proposes a distribution network confidence peak-shaving benefit evaluation method that takes into account the randomness of distributed photovoltaics, including the following steps:

(1) Obtain the grid parameters, load model and photovoltaic output model of the distribution network. The grid parameters include the line impedance of the distribution network and the impedance of each distribution transformer; the load model is a load probability model considering the load prediction error; The output model is a photovoltaic output probability model that considers the fluctuation and timing of photovoltaic output and the forecast error of weather type;

(2) Obtain the node number and access capacity of distributed photovoltaic access φ _i is the number set of distributed photovoltaic access nodes, is the access capacity set of corresponding nodes of distributed photovoltaic;

(3) According to the load model and photovoltaic output model, the Monte Carlo method is used to sample and simulate the daily load curve and photovoltaic output curve of each node, and the difference between the two is obtained to obtain the equivalent load curve sample of each node;

(4) Calculate the confidence daily kurtosis of each node according to the equivalent load curve sample and the original load curve sample;

(5) Calculate the peak-shaving benefits of substations, lines and distribution units, establish a probability evaluation model that simultaneously takes into account the total peak-shaving benefits of the three types of equipment in substations, lines and distribution transformers, and calculate the confidence daily peak-shaving benefits of the distribution network ;

(6) Repeat steps (3) to (5) to evaluate the confidence daily peak-shaving benefits of the distribution network under different weather types, and then consider the probability of occurrence of different weather types to evaluate the confidence daily peak-shaving benefit expectations of the distribution network .

In the above distribution network confidence peak-shaving benefit evaluation method considering the randomness of distributed photovoltaics, the node confidence peak-shaving degree refers to the maximum value of the node’s daily peak-shaving degree under the set confidence level, and the confidence of each node The calculation method of kurtosis is as follows:

(1) For the confidence daily kurtosis X _D,i of node i on the low-voltage side and high-voltage side of the distribution transformer, its calculation formula is as follows:

In the formula: α represents the set confidence; f _i (x) represents the probability _density function of the daily kurtosis x of the power delivered by node i under the consideration of photovoltaic output fluctuations _and load forecast errors; ) corresponding to the probability distribution function F _i (x) is the value of the independent variable determined under the condition that the function value is a set α confidence degree.

(2) For the confidence daily kurtosis X _D0 of the head-end node of the distribution network, the calculation method is ① calculate the eighth-order semi-invariant of each node according to the equivalent load curve sample of each node under the photovoltaic access condition, ② based on The probabilistic power flow calculation method of the semi-invariant method obtains the probability density function of the power transmitted by the head-end node under each section of the whole day. ③According to the probability density function of the power transmitted by the head-end node The maximum value of the curve is denoted as S _0.max.pv , and finally repeat steps ① to ③ to calculate the confidence day power curve without considering the PV access, the maximum value Recorded as S _0.max , then X _D0 =S _0.max -S _0.max.pv .

In the above distribution network confidence peak-shaving benefit evaluation method considering the randomness of distributed photovoltaics, the three types of equipment including substation peak-shaving benefit B _S , line peak-shaving benefit B _L and distribution transformer peak-shaving benefit B _T are simultaneously considered The probability evaluation model of the total peak-shaving benefit B _PC of the distribution network is:

B _PC ＝B _T +B _L +B _S (2)

in:

In the formula: α _i is a logical variable, indicating that the i node connected to the distributed photovoltaic takes the value of 1 when it is the 0.4kV low-voltage side bus of the distribution transformer, and takes the value of 0 when it is the 10kV high-voltage side bus of the distribution transformer; d _i is the i node Distribution transformer peak-shaving benefit per unit peak-shaving amount (unit is yuan/kW); R _T , R _S are the equivalent annual value coefficients of investment in distribution transformer and substation respectively; X _D,i represents the confidence daily kurtosis of node i ( The unit is kW); N _d is the number of grid-connected points of distributed photovoltaics; C _LMCC,i represents the annual value such as the node marginal capacity cost of node i, and its value is approximately the annual value such as the line peak-shaving benefit of the peak-shaving amount of each node unit (unit is yuan/kW); s ₀ is the substation peak-shaving benefit per unit peak-shaving amount of the head-end node of the line (unit is yuan/kW); X _D0 is the confidence daily kurtosis degree of the head-end node of the line.

The above-mentioned distribution network confidence daily peak-shaving benefit expectation The calculation method is as follows:

In the formula: B _PC,j represents the confidence day peak-shaving benefit under the jth weather type; P _j represents the probability of occurrence of the jth generalized weather type within a cycle; n is the number of generalized weather types.

Compared with prior art, the beneficial effect of the present invention is:

(1) By considering the impact of load and weather type prediction errors and the randomness of photovoltaic output on distribution network peak-shaving benefits, the distribution network peak-shaving benefits under a certain confidence level can be given, and the probability evaluation model is more realistic;

(2) The established distribution network peak-shaving benefit probability evaluation model can effectively identify the distributed photovoltaics connected to the distribution network at different locations and different voltage levels by considering the peak-shaving benefits of three types of equipment: substations, lines and distribution transformers. The model can be effectively used in the study of site selection and capacity planning of distributed photovoltaics.

Description of drawings

Fig. 1 is a schematic flow chart of the distribution network confidence peak-shaving benefit evaluation method considering the randomness of distributed photovoltaic provided by the present invention.

Figure 2 is a schematic diagram of a 10kV distribution network line topology.

Figure 3 is the load curve shape of different load types in the distribution network.

Detailed ways

The specific implementation of the present invention will be further described below in conjunction with accompanying drawings and examples. It should be pointed out that if there are no special details below (such as the symbols in Figure 2), those skilled in the art can understand or understand according to the prior art. Achieved.

Figure 1 reflects the specific process of the distribution network confidence peak shaving benefit evaluation method considering the randomness of distributed photovoltaics, including the following steps:

(1) Obtain the grid parameters, load model and photovoltaic output model of the distribution network. The grid parameters include the line impedance of the distribution network and the impedance of each distribution transformer; the load model is a load probability model considering the load prediction error; The output model is a photovoltaic output probability model that considers the fluctuation and timing of photovoltaic output and the forecast error of weather type;

(2) Obtain the node number and access capacity of distributed photovoltaic access φ _i is the number set of distributed photovoltaic access nodes, is the access capacity set of corresponding nodes of distributed photovoltaic;

(3) According to the load model and photovoltaic output model, the Monte Carlo method is used to sample and simulate the daily load curve and photovoltaic output curve of each node, and the difference between the two is obtained to obtain the equivalent load curve sample of each node;

(4) Calculate the confidence daily kurtosis of each node according to the equivalent load curve sample and the original load curve sample;

(5) Calculate the peak-shaving benefits of substations, lines and distribution units, establish a probability evaluation model that simultaneously takes into account the total peak-shaving benefits of the three types of equipment in substations, lines and distribution transformers, and calculate the confidence daily peak-shaving benefits of the distribution network ;

(6) Repeat steps (3) to (5) to evaluate the confidence daily peak-shaving benefits of the distribution network under different weather types, and then consider the probability of occurrence of different weather types to evaluate the confidence daily peak-shaving benefit expectations of the distribution network .

The following is an actual calculation example of the method of the present invention, taking a certain 10kV distribution network line as an example for evaluation and calculation, and Fig. 2 shows the topology structure of the distribution network.

(1) Obtain the network frame parameters of the distribution network as shown in Figure 2, in which the line type of the main line is LGJ-240, the line type of the branch line is LGJ-120, the line parameters are shown in Table 1, and the length of each branch is in As shown in the figure, the load of each node takes 96 sections throughout the day, the maximum value of active load throughout the day is 60% of the distribution transformer capacity, the load power factor is 0.95, the prediction error of the load output model is 5%, and the distribution network area is The probability of sunny days is 12.8%, the probability of cloudy days is 50.4%, and the probability of changing weather is 36.8%;

Table 1 Line parameters

(2) Obtain the node number and access capacity of distributed photovoltaic access as shown in Table 2, where the scene 1 is that the photovoltaic is connected to the industrial load area 1 at the end of the 10kV busbar access distribution network (as shown in Figure 2), and the other The load type of regional nodes is comprehensive load, and the curve shape of industrial load and comprehensive load is shown in Figure 3. In addition, scenarios 2 and 3 are set as control groups to evaluate the benefits of peak shaving. The following uses scenario 1 under sunny weather as an example to illustrate the evaluation steps.

Table 2 PV access information in different scenarios

(3) According to the load model and photovoltaic output model, the Monte Carlo method is used to sample 10,000 times, and the daily load curve and photovoltaic output curve of each node are simulated, and the difference between the two is obtained to obtain 10,000 samples of the equivalent load curve of each node.

(4) According to the equivalent load curve sample and the original load curve sample, take the confidence level as 0.9, and calculate the confidence daily kurtosis of each node in sunny weather, as shown in Table 3.

Table 3 Node confidence daily kurtosis

(5) According to the probability evaluation model of distribution network peak-shaving benefits, calculate the distribution network's peak-shaving benefits in sunny days.

(5-1) Calculate the peak-shaving benefit of the peak-shaving amount of the substation and distribution unit, the results are shown in Table 4, and the calculation formula of the average annual value coefficient is as follows:

In the formula, η represents the discount rate, which is 8%, and T is the investment return period of the equipment, and the values are shown in Table 4.

Table 4 Peak shaving benefit of each equipment unit peak shaving amount

(5-2) Calculate the node marginal capacity cost of each node, and its value is approximately the line peak-shaving benefit of the unit peak-shaving amount of each node. The results are shown in Table 5:

Table 5 Line peak shaving benefit per unit peak shaving amount

(5-3) Calculate the confidence day peak-shaving benefit of the distribution network in sunny weather, and the results are shown in the first row of scenario 1 in Table 6:

Table 6 Line peak shaving benefit per unit peak shaving amount

(6) Repeat steps (3) to (5) to calculate the daily peak-shaving benefits of the distribution network under different weather types. The results are shown in rows 2 and 3 of Scenario 1 in Table 6, and then consider the probability of occurrence of different weather types , calculate the confidence day peak-shaving benefit expectation of the distribution network, and the calculation results are shown in the fourth row of scenario 1 in Table 6.

In order to further reflect the beneficial effects of the present invention, Scene 2 and Scene 3 are added for comparison with Scene 1. Scene 2 is that the photovoltaic is connected to the industrial load area 2 in the middle of the distribution network at 10kV busbar, and Scene 3 is that the photovoltaic is at 0.4kV. The busbar is connected to the industrial load area 1 at the end of the distribution network. The photovoltaic access information in different scenarios is shown in Table 2 and Figure 2, and the evaluation results are shown in Table 6.

It can be seen from Table 6 that under different weather types, the confidence peak-shaving benefits of distributed photovoltaic distribution networks are quite different. 1 and Scenario 2, it can be seen that the peak-shaving benefit of distributed photovoltaic access to the end of the distribution network of the same capacity is greater than that of the middle of the distribution network, and the closer the photovoltaic access position is to the end of the line, the peak-shaving benefits of distribution transformers and lines The proportion of the total peak-shaving benefits will gradually increase, reaching a maximum of about 60%. Therefore, especially when photovoltaics are connected to the end of the line, the peak-shaving benefits of the line and distribution transformer cannot be ignored; it can be seen from scenarios 1 and 3 that the same The peak-shaving benefit of connecting distributed photovoltaics with capacity to the 0.4kV bus in the same area is about 50% higher than that of connecting to the 10kV bus; in conclusion, the evaluation model of the present invention considers the peak-shaving benefits of distribution transformers and lines, In terms of the total peak-shaving benefit of the distribution network, it has the ability to identify the access of distributed photovoltaics at different locations and voltage levels.

The above-mentioned embodiment is a preferred embodiment of the present invention, but the embodiment of the present invention is not limited by the above-mentioned embodiment, and any other modifications, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principles of the present invention , should be equivalent replacement methods, and should be included within the protection scope of the present invention.

Claims (4)

1. A distribution network confidence peak-shaving benefit evaluation method considering distributed photovoltaic randomness, characterized in that it comprises the following steps: (1) Obtain the grid parameters, load model and photovoltaic output model of the distribution network. The grid parameters include the line impedance of the distribution network and the impedance of each distribution transformer; the load model is a load probability model considering the load prediction error; The output model is a photovoltaic output probability model that considers the fluctuation and timing of photovoltaic output and the forecast error of weather type; (2) Obtain the node number and access capacity of distributed photovoltaic access φ _i is the number set of distributed photovoltaic access nodes, is the access capacity set of corresponding nodes of distributed photovoltaic; (3) According to the load model and photovoltaic output model, the Monte Carlo method is used to sample and simulate the daily load curve and photovoltaic output curve of each node, and the difference between the two is obtained to obtain the equivalent load curve sample of each node; (4) Calculate the confidence daily kurtosis of each node according to the equivalent load curve sample and the original load curve sample; (5) Calculate the peak-shaving benefits of substations, lines and distribution units, establish a probability evaluation model that simultaneously takes into account the total peak-shaving benefits of the three types of equipment in substations, lines and distribution transformers, and calculate the confidence daily peak-shaving benefits of the distribution network ; (6) Repeat steps (3) to (5) to evaluate the confidence daily peak-shaving benefits of the distribution network under different weather types, and then consider the probability of occurrence of different weather types to evaluate the confidence daily peak-shaving benefit expectations of the distribution network . 2. The distribution network confidence peak-shaving benefit evaluation method considering distributed photovoltaic randomness according to claim 1, characterized in that: the calculation method of the confidence daily peak-shaving degree of each node described in step (4) is as follows : (4.1) Firstly, define the node confidence daily kurtosis as the maximum value of node daily kurtosis under the set confidence level; (4.2) For the confidence daily kurtosis X _D, i of node i on the low-voltage side and high-voltage side of the distribution transformer, the calculation formula is as follows: In the formula: α represents the set confidence; f _i (x) represents the probability _density function of the _daily kurtosis x of the power delivered by node i under the consideration of photovoltaic output fluctuations and load forecast errors; ) corresponding to the probability distribution function F _i (x) is the value of the independent variable determined under the condition that the function value is the set α confidence degree; (4.3) For the confidence daily kurtosis X _D0 of the head-end node of the distribution network, the calculation method is ① calculate the eighth-order semi-invariant of each node according to the sample of the equivalent load curve of each node under the photovoltaic access condition, ② based on The probabilistic power flow calculation method of the semi-invariant method obtains the probability density function of the power transmitted by the head-end node under each section of the whole day. ③According to the probability density function of the power transmitted by the head-end node The maximum value of the curve is denoted as S _0.max.pv , and finally repeat steps ① to ③ to calculate the confidence day power curve without considering the PV access, the maximum value Recorded as S _0.max , then X _D0 =S _0.max -S _0.max.pv . 3. The distribution network confidence peak-shaving benefit evaluation method considering the randomness of distributed photovoltaics according to claim 1, characterized in that: the established in step (5) simultaneously takes into account the substation peak-shaving benefit B _S , line The probability _evaluation model of the total peak-shaving benefit B _PC of the distribution network for the three types _of equipment: B _PC ＝B _T +B _L +B _S (2) in: In the formula: α _i is a logical variable, indicating that the i node connected to the distributed photovoltaic takes the value of 1 when it is the 0.4kV low-voltage side bus of the distribution transformer, and takes the value of 0 when it is the 10kV high-voltage side bus of the distribution transformer; d _i is the i node Distribution transformer peak shaving benefit per unit peak shaving amount; R _T , R _S are the equivalent annual value coefficients of distribution transformer and substation investment respectively; X _D,i represents the daily peak shaving degree of node i confidence; N _d is distributed photovoltaic The number of grid-connected points; C _LMCC,i represents the node marginal capacity cost of node i, and its value is approximately the line peak-shaving benefit of the unit peak-shaving amount of each node; s ₀ is the substation peak-shaving benefit of the unit peak-shaving amount of the head-end node of the line; X _D0 is the confidence daily kurtosis of the headend node of the line. 4. The distribution network confidence peak-shaving benefit evaluation method considering distributed photovoltaic randomness according to claim 1, characterized in that: the distribution network confidence daily peak-shaving benefit expectation described in step (6) The calculation method is as follows: In the formula: B _PC,j represents the confidence day peak-shaving benefit under the jth weather type; P _j represents the probability of occurrence of the jth generalized weather type within a cycle; n is the number of generalized weather types.